Overview

Brought to you by YData

Dataset statistics

Number of variables14
Number of observations8760
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.8 MiB
Average record size in memory336.8 B

Variable types

DateTime1
Numeric10
Categorical2
Boolean1

Alerts

Dew point temperature(C) is highly overall correlated with Humidity(%) and 2 other fieldsHigh correlation
Humidity(%) is highly overall correlated with Dew point temperature(C)High correlation
Rented Bike Count is highly overall correlated with Temperature(C)High correlation
Seasons is highly overall correlated with Dew point temperature(C) and 1 other fieldsHigh correlation
Temperature(C) is highly overall correlated with Dew point temperature(C) and 2 other fieldsHigh correlation
Holiday is highly imbalanced (71.7%)Imbalance
Functioning Day is highly imbalanced (78.7%)Imbalance
Rented Bike Count has 295 (3.4%) zerosZeros
Hour has 365 (4.2%) zerosZeros
Solar Radiation (MJ/m2) has 4300 (49.1%) zerosZeros
Rainfall(mm) has 8232 (94.0%) zerosZeros
Snowfall (cm) has 8317 (94.9%) zerosZeros

Reproduction

Analysis started2024-09-04 18:38:32.141719
Analysis finished2024-09-04 18:38:44.863661
Duration12.72 seconds
Software versionydata-profiling vv4.9.0
Download configurationconfig.json

Variables

Date
Date

Distinct365
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Memory size68.6 KiB
Minimum2017-01-12 00:00:00
Maximum2018-12-11 00:00:00
2024-09-04T13:38:45.018562image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-04T13:38:45.250432image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Rented Bike Count
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct2166
Distinct (%)24.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean704.60205
Minimum0
Maximum3556
Zeros295
Zeros (%)3.4%
Negative0
Negative (%)0.0%
Memory size68.6 KiB
2024-09-04T13:38:45.409439image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile22
Q1191
median504.5
Q31065.25
95-th percentile2043
Maximum3556
Range3556
Interquartile range (IQR)874.25

Descriptive statistics

Standard deviation644.99747
Coefficient of variation (CV)0.91540674
Kurtosis0.85338699
Mean704.60205
Median Absolute Deviation (MAD)373.5
Skewness1.1534282
Sum6172314
Variance416021.73
MonotonicityNot monotonic
2024-09-04T13:38:45.559943image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 295
 
3.4%
122 19
 
0.2%
223 19
 
0.2%
262 19
 
0.2%
165 18
 
0.2%
103 18
 
0.2%
189 18
 
0.2%
178 17
 
0.2%
170 17
 
0.2%
71 17
 
0.2%
Other values (2156) 8303
94.8%
ValueCountFrequency (%)
0 295
3.4%
2 3
 
< 0.1%
3 2
 
< 0.1%
4 5
 
0.1%
5 3
 
< 0.1%
6 3
 
< 0.1%
7 4
 
< 0.1%
8 7
 
0.1%
9 12
 
0.1%
10 7
 
0.1%
ValueCountFrequency (%)
3556 1
< 0.1%
3418 1
< 0.1%
3404 1
< 0.1%
3384 1
< 0.1%
3380 1
< 0.1%
3365 1
< 0.1%
3309 1
< 0.1%
3298 1
< 0.1%
3277 1
< 0.1%
3256 1
< 0.1%

Hour
Real number (ℝ)

ZEROS 

Distinct24
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.5
Minimum0
Maximum23
Zeros365
Zeros (%)4.2%
Negative0
Negative (%)0.0%
Memory size68.6 KiB
2024-09-04T13:38:45.674912image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q15.75
median11.5
Q317.25
95-th percentile22
Maximum23
Range23
Interquartile range (IQR)11.5

Descriptive statistics

Standard deviation6.9225817
Coefficient of variation (CV)0.60196363
Kurtosis-1.2041763
Mean11.5
Median Absolute Deviation (MAD)6
Skewness0
Sum100740
Variance47.922137
MonotonicityNot monotonic
2024-09-04T13:38:45.779562image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
0 365
 
4.2%
1 365
 
4.2%
2 365
 
4.2%
3 365
 
4.2%
4 365
 
4.2%
5 365
 
4.2%
6 365
 
4.2%
7 365
 
4.2%
8 365
 
4.2%
9 365
 
4.2%
Other values (14) 5110
58.3%
ValueCountFrequency (%)
0 365
4.2%
1 365
4.2%
2 365
4.2%
3 365
4.2%
4 365
4.2%
5 365
4.2%
6 365
4.2%
7 365
4.2%
8 365
4.2%
9 365
4.2%
ValueCountFrequency (%)
23 365
4.2%
22 365
4.2%
21 365
4.2%
20 365
4.2%
19 365
4.2%
18 365
4.2%
17 365
4.2%
16 365
4.2%
15 365
4.2%
14 365
4.2%

Temperature(C)
Real number (ℝ)

HIGH CORRELATION 

Distinct546
Distinct (%)6.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.882922
Minimum-17.8
Maximum39.4
Zeros21
Zeros (%)0.2%
Negative1433
Negative (%)16.4%
Memory size68.6 KiB
2024-09-04T13:38:45.889741image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-17.8
5-th percentile-7.1
Q13.5
median13.7
Q322.5
95-th percentile30.7
Maximum39.4
Range57.2
Interquartile range (IQR)19

Descriptive statistics

Standard deviation11.944825
Coefficient of variation (CV)0.92718289
Kurtosis-0.83778629
Mean12.882922
Median Absolute Deviation (MAD)9.4
Skewness-0.19832553
Sum112854.4
Variance142.67885
MonotonicityNot monotonic
2024-09-04T13:38:46.018088image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20.5 40
 
0.5%
19.1 40
 
0.5%
23.4 39
 
0.4%
20.7 38
 
0.4%
7.6 38
 
0.4%
24.2 37
 
0.4%
20.2 35
 
0.4%
19.4 34
 
0.4%
19 34
 
0.4%
21.9 33
 
0.4%
Other values (536) 8392
95.8%
ValueCountFrequency (%)
-17.8 1
 
< 0.1%
-17.5 2
 
< 0.1%
-17.4 1
 
< 0.1%
-16.9 1
 
< 0.1%
-16.5 1
 
< 0.1%
-16.4 2
 
< 0.1%
-16.2 3
< 0.1%
-16.1 2
 
< 0.1%
-16 2
 
< 0.1%
-15.9 5
0.1%
ValueCountFrequency (%)
39.4 1
 
< 0.1%
39.3 1
 
< 0.1%
39 1
 
< 0.1%
38.7 1
 
< 0.1%
38 1
 
< 0.1%
37.9 2
 
< 0.1%
37.8 3
< 0.1%
37.6 1
 
< 0.1%
37.5 1
 
< 0.1%
37.4 6
0.1%

Humidity(%)
Real number (ℝ)

HIGH CORRELATION 

Distinct90
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean58.226256
Minimum0
Maximum98
Zeros17
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size68.6 KiB
2024-09-04T13:38:46.149251image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile27
Q142
median57
Q374
95-th percentile94
Maximum98
Range98
Interquartile range (IQR)32

Descriptive statistics

Standard deviation20.362413
Coefficient of variation (CV)0.34971188
Kurtosis-0.80355919
Mean58.226256
Median Absolute Deviation (MAD)16
Skewness0.059578973
Sum510062
Variance414.62788
MonotonicityNot monotonic
2024-09-04T13:38:46.276432image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
97 173
 
2.0%
53 173
 
2.0%
43 164
 
1.9%
57 159
 
1.8%
56 157
 
1.8%
47 156
 
1.8%
51 155
 
1.8%
63 153
 
1.7%
54 151
 
1.7%
52 150
 
1.7%
Other values (80) 7169
81.8%
ValueCountFrequency (%)
0 17
0.2%
10 1
 
< 0.1%
11 1
 
< 0.1%
12 1
 
< 0.1%
13 3
 
< 0.1%
14 16
0.2%
15 17
0.2%
16 15
0.2%
17 21
0.2%
18 15
0.2%
ValueCountFrequency (%)
98 50
 
0.6%
97 173
2.0%
96 111
1.3%
95 68
 
0.8%
94 54
 
0.6%
93 38
 
0.4%
92 27
 
0.3%
91 38
 
0.4%
90 52
 
0.6%
89 62
 
0.7%

Wind speed (m/s)
Real number (ℝ)

Distinct65
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.7249087
Minimum0
Maximum7.4
Zeros74
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size68.6 KiB
2024-09-04T13:38:46.397061image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.4
Q10.9
median1.5
Q32.3
95-th percentile3.7
Maximum7.4
Range7.4
Interquartile range (IQR)1.4

Descriptive statistics

Standard deviation1.0363
Coefficient of variation (CV)0.60078543
Kurtosis0.72717945
Mean1.7249087
Median Absolute Deviation (MAD)0.7
Skewness0.8909548
Sum15110.2
Variance1.0739177
MonotonicityNot monotonic
2024-09-04T13:38:46.520478image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.1 420
 
4.8%
1.2 403
 
4.6%
0.9 388
 
4.4%
1 388
 
4.4%
0.8 385
 
4.4%
1.4 355
 
4.1%
1.3 344
 
3.9%
1.5 343
 
3.9%
1.6 332
 
3.8%
0.6 321
 
3.7%
Other values (55) 5081
58.0%
ValueCountFrequency (%)
0 74
 
0.8%
0.1 49
 
0.6%
0.2 86
 
1.0%
0.3 158
1.8%
0.4 186
2.1%
0.5 258
2.9%
0.6 321
3.7%
0.7 313
3.6%
0.8 385
4.4%
0.9 388
4.4%
ValueCountFrequency (%)
7.4 1
 
< 0.1%
7.3 1
 
< 0.1%
7.2 1
 
< 0.1%
6.9 1
 
< 0.1%
6.7 1
 
< 0.1%
6.1 1
 
< 0.1%
6 2
< 0.1%
5.8 4
< 0.1%
5.7 1
 
< 0.1%
5.6 2
< 0.1%

Visibility (10m)
Real number (ℝ)

Distinct1789
Distinct (%)20.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1436.8258
Minimum27
Maximum2000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size68.6 KiB
2024-09-04T13:38:46.649109image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum27
5-th percentile300
Q1940
median1698
Q32000
95-th percentile2000
Maximum2000
Range1973
Interquartile range (IQR)1060

Descriptive statistics

Standard deviation608.29871
Coefficient of variation (CV)0.42336288
Kurtosis-0.96198013
Mean1436.8258
Median Absolute Deviation (MAD)302
Skewness-0.70178645
Sum12586594
Variance370027.32
MonotonicityNot monotonic
2024-09-04T13:38:46.785896image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2000 2245
 
25.6%
1995 34
 
0.4%
1989 28
 
0.3%
1985 28
 
0.3%
1999 28
 
0.3%
1996 27
 
0.3%
1992 26
 
0.3%
1998 25
 
0.3%
1990 23
 
0.3%
1981 23
 
0.3%
Other values (1779) 6273
71.6%
ValueCountFrequency (%)
27 1
< 0.1%
33 1
< 0.1%
34 1
< 0.1%
38 1
< 0.1%
53 1
< 0.1%
54 1
< 0.1%
59 1
< 0.1%
63 1
< 0.1%
66 2
< 0.1%
70 1
< 0.1%
ValueCountFrequency (%)
2000 2245
25.6%
1999 28
 
0.3%
1998 25
 
0.3%
1997 22
 
0.3%
1996 27
 
0.3%
1995 34
 
0.4%
1994 18
 
0.2%
1993 13
 
0.1%
1992 26
 
0.3%
1991 14
 
0.2%

Dew point temperature(C)
Real number (ℝ)

HIGH CORRELATION 

Distinct556
Distinct (%)6.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.0738128
Minimum-30.6
Maximum27.2
Zeros60
Zeros (%)0.7%
Negative3138
Negative (%)35.8%
Memory size68.6 KiB
2024-09-04T13:38:46.907194image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-30.6
5-th percentile-19.505
Q1-4.7
median5.1
Q314.8
95-th percentile22.405
Maximum27.2
Range57.8
Interquartile range (IQR)19.5

Descriptive statistics

Standard deviation13.060369
Coefficient of variation (CV)3.2059326
Kurtosis-0.75542951
Mean4.0738128
Median Absolute Deviation (MAD)9.7
Skewness-0.36729844
Sum35686.6
Variance170.57325
MonotonicityNot monotonic
2024-09-04T13:38:47.028151image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 60
 
0.7%
21.1 43
 
0.5%
14.3 40
 
0.5%
21.2 40
 
0.5%
8.9 39
 
0.4%
21.8 39
 
0.4%
21.3 38
 
0.4%
2.2 38
 
0.4%
20.2 37
 
0.4%
21.5 36
 
0.4%
Other values (546) 8350
95.3%
ValueCountFrequency (%)
-30.6 1
< 0.1%
-30.5 1
< 0.1%
-29.8 1
< 0.1%
-29.7 1
< 0.1%
-29.6 2
< 0.1%
-29.5 1
< 0.1%
-29.2 1
< 0.1%
-29.1 1
< 0.1%
-29 2
< 0.1%
-28.9 2
< 0.1%
ValueCountFrequency (%)
27.2 1
 
< 0.1%
26.8 2
< 0.1%
26.6 1
 
< 0.1%
26.3 1
 
< 0.1%
26.1 3
< 0.1%
26 2
< 0.1%
25.9 1
 
< 0.1%
25.8 2
< 0.1%
25.7 1
 
< 0.1%
25.6 2
< 0.1%

Solar Radiation (MJ/m2)
Real number (ℝ)

ZEROS 

Distinct345
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.56911073
Minimum0
Maximum3.52
Zeros4300
Zeros (%)49.1%
Negative0
Negative (%)0.0%
Memory size68.6 KiB
2024-09-04T13:38:47.150484image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.01
Q30.93
95-th percentile2.56
Maximum3.52
Range3.52
Interquartile range (IQR)0.93

Descriptive statistics

Standard deviation0.86874624
Coefficient of variation (CV)1.5264977
Kurtosis1.126433
Mean0.56911073
Median Absolute Deviation (MAD)0.01
Skewness1.5040397
Sum4985.41
Variance0.75472003
MonotonicityNot monotonic
2024-09-04T13:38:47.274894image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4300
49.1%
0.01 128
 
1.5%
0.02 82
 
0.9%
0.03 69
 
0.8%
0.06 61
 
0.7%
0.05 54
 
0.6%
0.04 47
 
0.5%
0.11 44
 
0.5%
0.07 37
 
0.4%
0.16 36
 
0.4%
Other values (335) 3902
44.5%
ValueCountFrequency (%)
0 4300
49.1%
0.01 128
 
1.5%
0.02 82
 
0.9%
0.03 69
 
0.8%
0.04 47
 
0.5%
0.05 54
 
0.6%
0.06 61
 
0.7%
0.07 37
 
0.4%
0.08 33
 
0.4%
0.09 32
 
0.4%
ValueCountFrequency (%)
3.52 2
< 0.1%
3.49 1
 
< 0.1%
3.45 1
 
< 0.1%
3.44 1
 
< 0.1%
3.42 4
< 0.1%
3.41 2
< 0.1%
3.39 3
< 0.1%
3.38 1
 
< 0.1%
3.36 4
< 0.1%
3.35 1
 
< 0.1%

Rainfall(mm)
Real number (ℝ)

ZEROS 

Distinct61
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.14868721
Minimum0
Maximum35
Zeros8232
Zeros (%)94.0%
Negative0
Negative (%)0.0%
Memory size68.6 KiB
2024-09-04T13:38:47.398382image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.4
Maximum35
Range35
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.128193
Coefficient of variation (CV)7.5876932
Kurtosis284.9911
Mean0.14868721
Median Absolute Deviation (MAD)0
Skewness14.533232
Sum1302.5
Variance1.2728194
MonotonicityNot monotonic
2024-09-04T13:38:48.132770image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 8232
94.0%
0.5 116
 
1.3%
1 66
 
0.8%
1.5 56
 
0.6%
0.1 46
 
0.5%
2 31
 
0.4%
2.5 23
 
0.3%
0.2 20
 
0.2%
3.5 18
 
0.2%
0.4 16
 
0.2%
Other values (51) 136
 
1.6%
ValueCountFrequency (%)
0 8232
94.0%
0.1 46
 
0.5%
0.2 20
 
0.2%
0.3 9
 
0.1%
0.4 16
 
0.2%
0.5 116
 
1.3%
0.7 1
 
< 0.1%
0.8 3
 
< 0.1%
0.9 3
 
< 0.1%
1 66
 
0.8%
ValueCountFrequency (%)
35 1
< 0.1%
29.5 1
< 0.1%
24 1
< 0.1%
21.5 1
< 0.1%
21 1
< 0.1%
19 1
< 0.1%
18.5 2
< 0.1%
18 2
< 0.1%
17 1
< 0.1%
16 1
< 0.1%

Snowfall (cm)
Real number (ℝ)

ZEROS 

Distinct51
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.075068493
Minimum0
Maximum8.8
Zeros8317
Zeros (%)94.9%
Negative0
Negative (%)0.0%
Memory size68.6 KiB
2024-09-04T13:38:48.279796image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.2
Maximum8.8
Range8.8
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.43674618
Coefficient of variation (CV)5.8179692
Kurtosis93.803324
Mean0.075068493
Median Absolute Deviation (MAD)0
Skewness8.4408008
Sum657.6
Variance0.19074723
MonotonicityNot monotonic
2024-09-04T13:38:48.395793image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 8317
94.9%
0.3 42
 
0.5%
1 39
 
0.4%
0.5 34
 
0.4%
0.9 34
 
0.4%
0.7 31
 
0.4%
0.8 22
 
0.3%
2 22
 
0.3%
0.4 21
 
0.2%
1.6 19
 
0.2%
Other values (41) 179
 
2.0%
ValueCountFrequency (%)
0 8317
94.9%
0.1 2
 
< 0.1%
0.2 15
 
0.2%
0.3 42
 
0.5%
0.4 21
 
0.2%
0.5 34
 
0.4%
0.6 15
 
0.2%
0.7 31
 
0.4%
0.8 22
 
0.3%
0.9 34
 
0.4%
ValueCountFrequency (%)
8.8 2
< 0.1%
7.1 1
 
< 0.1%
7 1
 
< 0.1%
6 1
 
< 0.1%
5.1 1
 
< 0.1%
5 2
< 0.1%
4.8 2
< 0.1%
4.3 2
< 0.1%
4.2 1
 
< 0.1%
4.1 4
< 0.1%

Seasons
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size539.1 KiB
Spring
2208 
Summer
2208 
Autumn
2184 
Winter
2160 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters52560
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWinter
2nd rowWinter
3rd rowWinter
4th rowWinter
5th rowWinter

Common Values

ValueCountFrequency (%)
Spring 2208
25.2%
Summer 2208
25.2%
Autumn 2184
24.9%
Winter 2160
24.7%

Length

2024-09-04T13:38:48.503149image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-04T13:38:48.599161image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
spring 2208
25.2%
summer 2208
25.2%
autumn 2184
24.9%
winter 2160
24.7%

Most occurring characters

ValueCountFrequency (%)
m 6600
12.6%
r 6576
12.5%
u 6576
12.5%
n 6552
12.5%
S 4416
8.4%
i 4368
8.3%
e 4368
8.3%
t 4344
8.3%
p 2208
 
4.2%
g 2208
 
4.2%
Other values (2) 4344
8.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 52560
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
m 6600
12.6%
r 6576
12.5%
u 6576
12.5%
n 6552
12.5%
S 4416
8.4%
i 4368
8.3%
e 4368
8.3%
t 4344
8.3%
p 2208
 
4.2%
g 2208
 
4.2%
Other values (2) 4344
8.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 52560
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
m 6600
12.6%
r 6576
12.5%
u 6576
12.5%
n 6552
12.5%
S 4416
8.4%
i 4368
8.3%
e 4368
8.3%
t 4344
8.3%
p 2208
 
4.2%
g 2208
 
4.2%
Other values (2) 4344
8.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 52560
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
m 6600
12.6%
r 6576
12.5%
u 6576
12.5%
n 6552
12.5%
S 4416
8.4%
i 4368
8.3%
e 4368
8.3%
t 4344
8.3%
p 2208
 
4.2%
g 2208
 
4.2%
Other values (2) 4344
8.3%

Holiday
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size572.0 KiB
No Holiday
8328 
Holiday
 
432

Length

Max length10
Median length10
Mean length9.8520548
Min length7

Characters and Unicode

Total characters86304
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo Holiday
2nd rowNo Holiday
3rd rowNo Holiday
4th rowNo Holiday
5th rowNo Holiday

Common Values

ValueCountFrequency (%)
No Holiday 8328
95.1%
Holiday 432
 
4.9%

Length

2024-09-04T13:38:48.714709image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-04T13:38:48.809281image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
holiday 8760
51.3%
no 8328
48.7%

Most occurring characters

ValueCountFrequency (%)
o 17088
19.8%
a 8760
10.2%
H 8760
10.2%
l 8760
10.2%
i 8760
10.2%
y 8760
10.2%
d 8760
10.2%
N 8328
9.6%
8328
9.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 86304
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 17088
19.8%
a 8760
10.2%
H 8760
10.2%
l 8760
10.2%
i 8760
10.2%
y 8760
10.2%
d 8760
10.2%
N 8328
9.6%
8328
9.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 86304
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 17088
19.8%
a 8760
10.2%
H 8760
10.2%
l 8760
10.2%
i 8760
10.2%
y 8760
10.2%
d 8760
10.2%
N 8328
9.6%
8328
9.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 86304
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 17088
19.8%
a 8760
10.2%
H 8760
10.2%
l 8760
10.2%
i 8760
10.2%
y 8760
10.2%
d 8760
10.2%
N 8328
9.6%
8328
9.6%

Functioning Day
Boolean

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.7 KiB
True
8465 
False
 
295
ValueCountFrequency (%)
True 8465
96.6%
False 295
 
3.4%
2024-09-04T13:38:48.895073image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Interactions

2024-09-04T13:38:43.553444image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-04T13:38:32.646590image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-04T13:38:33.704543image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-04T13:38:35.049555image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-04T13:38:36.185483image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-04T13:38:37.394182image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-04T13:38:38.424254image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-04T13:38:39.934430image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-04T13:38:41.086212image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-04T13:38:42.090359image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-04T13:38:43.655079image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-04T13:38:32.739671image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-04T13:38:33.811862image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-04T13:38:35.168535image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-04T13:38:36.291109image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-04T13:38:37.508190image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-04T13:38:38.530687image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-04T13:38:40.077216image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-04T13:38:41.186197image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-04T13:38:42.369413image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-04T13:38:43.747766image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-04T13:38:32.835637image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-04T13:38:33.897938image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-04T13:38:35.259816image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-04T13:38:36.371166image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-04T13:38:37.591828image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-04T13:38:38.619576image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-04T13:38:40.189986image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-04T13:38:41.271414image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-04T13:38:42.544915image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-04T13:38:43.834636image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-04T13:38:32.954808image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-04T13:38:34.003850image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-04T13:38:35.353233image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-04T13:38:36.460029image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-04T13:38:37.686698image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-04T13:38:38.721776image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-04T13:38:40.302074image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-04T13:38:41.368778image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-04T13:38:42.711628image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-04T13:38:43.918792image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-04T13:38:33.054623image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-04T13:38:34.261659image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-04T13:38:35.469228image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-04T13:38:36.548179image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-04T13:38:37.791425image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-04T13:38:38.818306image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-04T13:38:40.428049image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-04T13:38:41.470413image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-04T13:38:42.863670image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-04T13:38:44.018678image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-04T13:38:33.160103image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-04T13:38:34.500432image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-04T13:38:35.630616image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-04T13:38:36.642821image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-04T13:38:37.931803image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-04T13:38:38.916796image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-04T13:38:40.566560image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-04T13:38:41.578089image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-04T13:38:43.004450image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-04T13:38:44.112994image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-04T13:38:33.272626image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-04T13:38:34.647261image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-04T13:38:35.777065image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-04T13:38:36.744144image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-04T13:38:38.037864image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-04T13:38:39.013200image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-04T13:38:40.672737image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-04T13:38:41.680249image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-04T13:38:43.140640image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-04T13:38:44.200996image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-04T13:38:33.369653image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-04T13:38:34.755326image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-04T13:38:35.892509image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-04T13:38:36.917715image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-04T13:38:38.137562image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-04T13:38:39.108367image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-04T13:38:40.767330image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-04T13:38:41.776376image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-04T13:38:43.252025image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-04T13:38:44.301420image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-04T13:38:33.481118image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-04T13:38:34.858984image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-04T13:38:36.004435image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-04T13:38:37.153072image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-04T13:38:38.238137image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-04T13:38:39.207220image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-04T13:38:40.869513image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-04T13:38:41.871564image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-04T13:38:43.357289image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-04T13:38:44.432149image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-04T13:38:33.592860image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-04T13:38:34.968870image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-04T13:38:36.097323image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-04T13:38:37.289636image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-04T13:38:38.337646image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-04T13:38:39.821045image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-04T13:38:40.977663image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-04T13:38:41.967054image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-04T13:38:43.460389image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2024-09-04T13:38:48.963968image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Dew point temperature(C)Functioning DayHolidayHourHumidity(%)Rainfall(mm)Rented Bike CountSeasonsSnowfall (cm)Solar Radiation (MJ/m2)Temperature(C)Visibility (10m)Wind speed (m/s)
Dew point temperature(C)1.0000.2320.1180.0030.5210.2130.3740.618-0.2490.0940.912-0.129-0.128
Functioning Day0.2321.0000.0240.0000.0680.0000.2190.2580.0070.0200.1950.0330.000
Holiday0.1180.0241.0000.0000.0790.0000.0990.1170.0220.0000.1460.0760.047
Hour0.0030.0000.0001.000-0.251-0.0260.3890.000-0.0320.2090.1210.0940.307
Humidity(%)0.5210.0680.079-0.2511.0000.368-0.2210.1840.050-0.4380.154-0.483-0.355
Rainfall(mm)0.2130.0000.000-0.0260.3681.000-0.2820.0260.002-0.0910.072-0.232-0.052
Rented Bike Count0.3740.2190.0990.389-0.221-0.2821.0000.313-0.2210.3820.5650.1760.148
Seasons0.6180.2580.1170.0000.1840.0260.3131.0000.1510.1330.6420.1360.110
Snowfall (cm)-0.2490.0070.022-0.0320.0500.002-0.2210.1511.000-0.077-0.307-0.0740.029
Solar Radiation (MJ/m2)0.0940.0200.0000.209-0.438-0.0910.3820.133-0.0771.0000.3280.0490.363
Temperature(C)0.9120.1950.1460.1210.1540.0720.5650.642-0.3070.3281.0000.0460.011
Visibility (10m)-0.1290.0330.0760.094-0.483-0.2320.1760.136-0.0740.0490.0461.0000.154
Wind speed (m/s)-0.1280.0000.0470.307-0.355-0.0520.1480.1100.0290.3630.0110.1541.000

Missing values

2024-09-04T13:38:44.556901image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-09-04T13:38:44.771048image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

DateRented Bike CountHourTemperature(C)Humidity(%)Wind speed (m/s)Visibility (10m)Dew point temperature(C)Solar Radiation (MJ/m2)Rainfall(mm)Snowfall (cm)SeasonsHolidayFunctioning Day
001/12/20172540-5.2372.22000-17.60.000.00.0WinterNo HolidayYes
101/12/20172041-5.5380.82000-17.60.000.00.0WinterNo HolidayYes
201/12/20171732-6.0391.02000-17.70.000.00.0WinterNo HolidayYes
301/12/20171073-6.2400.92000-17.60.000.00.0WinterNo HolidayYes
401/12/2017784-6.0362.32000-18.60.000.00.0WinterNo HolidayYes
501/12/20171005-6.4371.52000-18.70.000.00.0WinterNo HolidayYes
601/12/20171816-6.6351.32000-19.50.000.00.0WinterNo HolidayYes
701/12/20174607-7.4380.92000-19.30.000.00.0WinterNo HolidayYes
801/12/20179308-7.6371.12000-19.80.010.00.0WinterNo HolidayYes
901/12/20174909-6.5270.51928-22.40.230.00.0WinterNo HolidayYes
DateRented Bike CountHourTemperature(C)Humidity(%)Wind speed (m/s)Visibility (10m)Dew point temperature(C)Solar Radiation (MJ/m2)Rainfall(mm)Snowfall (cm)SeasonsHolidayFunctioning Day
875030/11/2018761147.8202.22000-13.81.670.00.0AutumnNo HolidayYes
875130/11/2018768157.0203.31994-14.41.210.00.0AutumnNo HolidayYes
875230/11/2018837167.2231.51945-12.60.720.00.0AutumnNo HolidayYes
875330/11/20181047176.0292.11877-10.70.230.00.0AutumnNo HolidayYes
875430/11/20181384184.7341.91661-9.80.000.00.0AutumnNo HolidayYes
875530/11/20181003194.2342.61894-10.30.000.00.0AutumnNo HolidayYes
875630/11/2018764203.4372.32000-9.90.000.00.0AutumnNo HolidayYes
875730/11/2018694212.6390.31968-9.90.000.00.0AutumnNo HolidayYes
875830/11/2018712222.1411.01859-9.80.000.00.0AutumnNo HolidayYes
875930/11/2018584231.9431.31909-9.30.000.00.0AutumnNo HolidayYes